Research

My primary area of research is machine learning, with an emphasis on learning useful data representations, and learning accurate classification models under various circumstances. My research goal is to automate the learning process and reduce the dependence of learning systems on human guidance. Natural language processing, computer vision and medical science are my application areas. My research covers the following topics:

• Generalized information adaptation: domain adaptation, zero-shot learning, continue learning
• Learning with limited and weak supervision: exemplar learning, few-shot learning, partial label learning
• Reinforcement learning: generalization and safe learning
• Learning with complex outputs: multi-label learning, sequence labeling
• Heterogeneous learning: multi-label, multi-view, multi-instance learning
• Representation learning: dimensionality reduction and feature selection, deep learning
• Active learning
• Learning recommender systems
• Learning graphical models
• Optimization

Current Group Members

• Abdullah Alchihabi (PhD Student)
• Hao Yan (PhD Student)
• Hanping Zhang (PhD Student)
• Marzi Heidari (PhD Student)
• Luke Budny (MSc. Student; Co-supervised with Dr. K. Cheung)
• Adam Koziak (MSc. Student)

Alumni

• Qing En (Carleton Univ., PDF, 2024)
• Yan Yan (Carleton Univ., PDF, 2024)
• Zainab Albujasim (Carleton Univ., PhD, 2023)
• Xuejun Han (Carleton Univ., PhD, 2023)
• Chen Shen (Temple Univ., PhD, 2020; now at Megagon Labs)
• Meng Ye (Temple Univ., PhD, 2019; now at SRI International)
• Feipeng Zhao (Temple Univ., PhD, 2017; now at ADP)
• Xin Li (Temple Univ., PhD, 2015; now at UKG)
• Min Xiao (Temple Univ., PhD, 2015; now at Microsoft)
• Suicheng Gu (Temple Univ., PDF 2010; now at Google)

• Taoseef Ishtiak (Carleton Univ., M.Sc., 2023)
• Vasileios Lioutas (Carleton Univ., M.Sc. 2020)
• Kevin Hua (Carleton Univ., M.Sc. 2020)
• Mahan Niknafs (Carleton Univ., M.Sc. 2020)
• Yaser Alwattar (Carleton Univ., M.Sc. 2019)
• Xinyuan Lu (Carleton Univ., M.Sc. 2019)
• Olivier Hamel (Carleton Univ., M.Sc. 2019)
• Xiangwei Meng (Temple Univ., M.Sc. 2015)
• Richard Hart (Temple Univ., M.Sc. 2011)

• Carter Black (Carleton Univ., undergraduate, 2021)
• Hammad Asad (Carleton Univ., undergraduate, 2017)
• Chris Abbott (Carleton Univ., undergraduate, 2017)
• Dabeluchi Ndubisi (Carleton Univ., undergraduate, 2017)

• Wentao Cui (Carleton Univ., Part-time PhD Student, 2018 -- 2021)
• Yang Wei (NJUST, visiting PhD student, 2019.12 -- 2021.5)
• Bingyu Liu (PhD student Intern., 2019 -- 2020)
• Zhen Zhao (M.Sc student Intern., 2019 -- 2020)
• Yan Yan (Northwestern Polytechnical Univ., visiting PhD student, 2018 -- 2020)
• Zengmao Wang (Wuhan Univ., visiting PhD student, 2017 -- 2018)
• Kongming Liang (Univ. of CAS, visiting PhD student, 2016 -- 2017)

Papers ( Google Scholar)

• M. Heidari, H. Zhang, and Y. Guo (2024),   ``Reinforcement Learning Guided Semi-Supervised Learning". Advances in Neural Information Processing Systems (NeurIPS).
  
  
• A. Alchihabi, M. Heidari, and Y. Guo (2024),   ``Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning". British Machine Vision Conference (BMVC).
  
  
• Q. En and Y. Guo (2024),   ``Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation". British Machine Vision Conference (BMVC).
  
  
• H. Yan and Y. Guo (2024),   ``Local and Global Flatness for Federated Domain Generalization". The European Conference on Computer Vision (ECCV).
  
  
• Q. En and Y. Guo (2024),   ``AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area Segmentation". European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).
  
  
• Q. En and Y. Guo (2024),   ``Unsupervised Domain Adaptation for Medical Image Segmentation with Dynamic Prototype-based Contrastive Learning". Conference on Health, Inference, and Learning (CHIL).
  
  
• M. Heidari, A. Alchihabi, Q. En, and Y. Guo (2024),   ``Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification". International Conference on Artificial Intelligence and Statistics (AISTATS).
  
  
• Q. En and Y. Guo (2024),   ``Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation". International Conference on Artificial Intelligence and Statistics (AISTATS).
  
  
• Y. Yan and Y. Guo (2024),   ``Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization". AAAI Conference on Artificial Intelligence (AAAI).
  
  
• A. Alchihabi, Q. En, and Y. Guo (2023),   ``Efficient Low-Rank GNN Defense Against Structural Attacks". IEEE International Conference On Knowledge Graph (ICKG).
  
  
• H. Yan and Y. Guo (2023),   ``Context-Aware Self-Adaptation for Domain Generalization". In the Second ICML Workshop on New Frontiers in Adversarial Machine Learning (AdvML-Frontiers).
  
  
• X. Han and Y. Guo (2023),   ``Evolving Dictionary Representation for Few-shot Class-incremental Learning". European Conference on Artificial Intelligence (ECAI).
  
  
• A. Alchihabi and Y. Guo (2023),   ``GDM: Dual Mixup for Graph Classification with Limited Supervision". European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD).
  
  
• Z. Albujasim, D. Inkpen, X. Han and Y. Guo (2023),   ``Improving Word Embedding Using Variational Dropout". International FLAIRS Conference (FLAIRS-36).
  
  
• T. Ishtiak, Q. En, and Y. Guo (2023),   ``Exemplar-FreeSOLO: Enhancing Unsupervised Instance Segmentation with Exemplars". IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  
  
• Y. Yan and Y. Guo (2023),   ``Mutual Partial Label Learning with Competitive Label Noise". International Conference on Learning Representations (ICLR).
  
  
• Y. Yan and Y. Guo (2023),   ``Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment". International Conference on Learning Representations (ICLR).
  
  
• A. Alchihabi and Y. Guo (2023),   ``Learning Robust Graph Neural Networks with Limited Supervision". International Conference on Artificial Intelligence and Statistics (AISTATS).
  
  
• Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye (2023),   ``Object Detection in 20 Years: A Survey". Proceedings of the IEEE.
  
  
• A. Alchihabi and Y. Guo (2022),   ``Dual GNNs: Learning Graph Neural Networks with Limited Supervision". In NeurIPS Workshop on Graph Learning for Industrial Applications (GLIndA).
  
  
• Z. Albujasim, D. Inkpen, and Y. Guo (2022),   ``Word Embedding Interpretation Using Co-Clustering". International Conference on Data Science and Cloud Computing (DSCC).
  
  
• X. Han and Y. Guo (2022),   ``Overcoming Catastrophic Forgetting for Continual Learning via Feature Propagation". British Machine Vision Conference (BMVC-22).
  
  
• H. Yan and Y. Guo (2022),   ``Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation". British Machine Vision Conference (BMVC-22).
  
  
• Q. En and Y. Guo (2022),   ``Exemplar Learning for Medical Image Segmentation". British Machine Vision Conference (BMVC-22).
  
  
• H. Zhang and Y. Guo (2022),   ``Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation". In Decision Awareness in Reinforcement Learning Workshop at ICML-22.
  
  
• H. Yan, Y. Guo, and C. Yang (2021),   ``Augmented Self-Labeling for Source-Free Unsupervised Domain Adaptation". In the Distribution Shifts (DistShift) Workshop of NeurIPS 2021.
  
  
• B. Liu, Y. Guo, J. Ye, and W. Deng (2021),   ``Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation". In Proceedings of the British Machine Vision Conference (BMVC-21).
  
  
• H. Yan, Y. Guo, and C. Yang (2021),   ``Source-free Unsupervised Domain Adaptation with Surrogate Data Generation". In Proceedings of the British Machine Vision Conference (BMVC-21).
  
  
• X. Han and Y. Guo (2021),   ``Continual Learning with Dual Regularizations''. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD-21). ( First Runner-up Student Machine Learning Paper Award)
  
  
• B. Liu, Y. Guo, J. Jiang, J. Tang, and W. Deng (2021),   ``Multi-view Correlation based Black-box Adversarial Attack for 3D Object Detection". ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-21).
  
  
• W. Cui and Y. Guo (2021),   ``Parameterless Transductive Feature Re-representation for Few-Shot Learning". Inernational Conference on Machine Learning (ICML-21).
  
  
• Y. Yan and Y. Guo (2021),   ``Multi-level Generative Models for Partial Label Learning with Non-random Label Noise". Inernational Joint Conference on Artificial Intelligence (IJCAI-21).
  
  
• Y. Yan and Y. Guo (2021),   ``Adversarial Partial Multi-Label Learning with Label Disambiguation". In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).
  
  
• Z. Zhao, Y. Guo, H. Shen, and J. Ye (2020),   ``Adaptive Object Detection with Dual Multi-Label Prediction". In Proceedings of the European Conference on Computer Vision (ECCV-20).
  
  
• Z. Zhao, Y. Guo and J. Ye (2020),   ``Bi-Dimensional Feature Alignment for Cross-Domain Object Detection". In Proceedings of the TASK-CV Workshop at ECCV 2020. (Best Paper Award)
  
  
• B. Liu, Z. Zhao, Z. Li, J. Jiang, Y. Guo and J. Ye (2020),   ``Feature Transformation Ensemble Model with Batch Spectral Regularizaion for Cross-Domain Few-Shot Classification". Winner of the CD-FSL Challenge at CVPR 2020.
  
  
• Y. Alwattar and Y. Guo (2020),   ``Inverse Visual Question Answering with Multi-Level Attentions". In Proceedings of the Asian Conference on Machine Learning (ACML-20).
  
  
• V. Lioutas and Y. Guo (2020),   ``Time-aware Large Kernel Convolutions". In Proceedings of the International Conference on Machine Learning (ICML-20).
  
  
• Y. Yan and Y. Guo (2020),   ``Partial Label Learning with Batch Label Correction". In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20).
  
  
• Y. Wu and Y. Guo (2020),   ``Dual Adversarial Co-Learning for Multi-Domain Text Classification". In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20).
  
  
• M. Zhang, C. Desrosiers, Y. Guo, B. Khundrakpam, N. AI-Sharif, G. Kiar, P. Valdes-Sosa, J.-B. Poline and A. Evans (2019),   ``Brain Status Modeling with Non-negative Projective Dictionary Learning". NeuroImage, 2019
  
  
• Z. Wang, B. Du, and Y. Guo (2019),   ``Domain Adaptation with Neural Embedding Matching". IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.
  
  
• M. Ye and Y. Guo (2019),   ``Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection". In Proceedings of the IEEE International Conference on Image Processing (ICIP-19).
  
  
• M. Ye and Y. Guo (2019),   ``Progressive Ensemble Networks for Zero Shot Recognition". In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-19).
  
  
• H. Zhao, H. Li, S. Maurer-Stroh, Y. Guo, Q. Deng an L. Cheng (2019),  ``Supervised Segmentation of Un-annotated Retinal Fundus Images by Synthesis". IEEE Transactions on Medical Imaging, 38(1): 46-56, 2019.
  
  
• C. Shen and Y. Guo (2018),   ``Unsupervised Heterogeneous Domain Adaptation with Sparse Feature Transformation". In Proceedings of the Asian Conference on Machine Learning (ACML-18).
  
  
• M. Ye and Y. Guo (2018),   ``Deep Triplet Ranking Networks for One-Shot Recognition". arXiv:1804.07275
  
  
• Z. Wang, Y. Guo, and B. Du (2018),   ``Matrix Completion with Preference Ranking for Top- N Recommendation". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-18).
  
  
• M. Zhang, C. Desrosiers, Y. Guo, C. Zhang, B. Khundrakpam, and A. Evans (2018),   ``Brain Status Prediction with Non-negative Projective Dictionary Learning". In Proceedings of the International Conference on Machine Learning in Medical Imaging (MLMI-18).
  
  
• K. Liang, Y. Guo, H. Chang, and X. Chen (2018),   ``Visual Relationship Detection with Deep Structural Ranking". In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18).
  
  
• M. Ye and Y. Guo (2017),   ``Labelless Scene Classification". In NIPS Workshop on Visually-Grounded Interaction and Language (ViGIL). (This is a short version of our BMVC paper).
  
  
• Q. Liu, Y. Guo, J. Wu and G. Wang (2017),   ``Effective Query Grouping Strategy in Clouds". In Journal of Computer Science and Technology. 36(6): 1231-1249, 2017.
  
  
• M. Ye and Yuhong Guo (2017),   ``Labelless Scene Classification with Semantic Matching". In Proceedings of the British Machine Vision Conference (BMVC-17).  [pdf]
  
  
• K. Liang, Yuhong Guo, H. Chang, and X. Chen (2017),   ``Incomplete Attribute Learning with Auxiliary Labels". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-17).  [pdf]
  
  
• F. Zhao and Yuhong Guo (2017),   ``Learning Discriminative Recommendation Systems with Side Information". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-17).  [pdf]
  
  
• M. Ye and Yuhong Guo (2017),   ``Zero-Shot Classification with Discriminative Semantic Representation Learning". In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-17).  [pdf]
  
  
• Yuhong Guo (2017),  ``Convex Co-Embedding for Matrix Completion with Predictive Side Information". In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).  [pdf]
  
  
• F. Zhao and Yuhong Guo (2016),   ``Improving Top-N Recommendation with Heterogeneous Loss". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-16).  [pdf]
  
  
• F. Zhao, M. Xiao and Yuhong Guo (2016),   ``Predictive Collaborative Filtering with Side Information". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-16).  [pdf]
  
  
• X. Li, Yuhong Guo and D. Schuurmans (2015),  ``Semi-Supervised Zero-Shot Classification with Label Representation Learning". In Proceedings of the IEEE International Conference on Computer Vision (ICCV-15).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2015),  ``Annotation Projection-based Representation Learning for Cross-lingual Dependency Parsing". `` In Proceedings of the Conference on Computational Natural Language Learning (CoNLL-15)  [pdf]
  
  
• M. Xiao and Yuhong Guo (2015),  ``Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation". In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL-15).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2015),  ``Semi-Supervised Subspace Co-Projection for Multi-Class Heterogeneous Domain Adaptation". In Proceedings of the European Conference on Machine Learning (ECML-15).  [pdf]
  
  
• F. Zhao and Yuhong Guo (2015),   ``Semi-supervised Multi-label Learning with Incomplete Labels". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-15).  [pdf]
  
  
• X. Li and Yuhong Guo (2015),   ``Multi-label Classification with Feature-aware Non-linear Label Space Transformation". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-15).  [pdf]
  
  
• X. Li and Yuhong Guo (2015),  ``Max-Margin Zero-Shot Learning for Multi-class Classification". In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS-15).  [pdf]
  
  
• X. Li, F. Zhao and Yuhong Guo (2015),  ``Conditional Restricted Boltzmann Machines for Multi-label Learning with Incomplete Labels". In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS-15).  [pdf]
  
  
• X. Li and Yuhong Guo (2014),  ``Multi-level Adaptive Active Learning for Scene Classication". In Proceedings of the European Conference on Computer Vision (ECCV-14).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2014),   ``Feature Space Independent Semi-Supervised Domain Adaptation via Kernel Matching". IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI-14), Volume 37, Issue 1, Pages 54-66.  [pdf]
  
  
• X. Li and Yuhong Guo (2014),  ``Bi-Directional Representation Learning for Multi-label Classification". In Proceedings of the European Conference on Machine Learning (ECML-14).  [pdf]
  
  
• X. Li, F. Zhao and Yuhong Guo (2014),   ``Multi-label Image Classification with A Probabilistic Label Enhancement Model". In Proceedings of the thirtieth Conference on Uncertainty in Artificial Intelligence (UAI-14).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2014),   ``Distributed Word Representation Learning for Cross-lingual Dependency Parsing". In Proceedings of the Conference on Computational Natural Language Learning (CoNLL-14).  [pdf]
  
  
• X. Li and Yuhong Guo (2014),   ``Latent Semantic Representation Learning for Scene Classification". In Proceedings of the International Conference on Machine Learning (ICML-14).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2014),  ``Semi-supervised Matrix Completion for Cross-Lingual Text Classification". In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI-14).  [pdf]
  
  
• F. Mirzazadeh, Yuhong Guo, and D. Schuurmans (2014),  ``Convex Co-embedding". In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI-14).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2013),   ``A Novel Two-Step Method for Cross Language Representation Learning". In Advances in Neural Information Processing Systems (NIPS-13).  [pdf]
  
  
• Yuhong Guo (2013),   ``Robust Transfer Principal Component Analysis with Rank Constraints". In Advances in Neural Information Processing Systems (NIPS-13).  [pdf]
  
  
• M. Xiao, F. Zhao and Yuhong Guo (2013),   ``Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering". In Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP-13).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2013),   ``Semi-Supervised Representation Learning for Cross-Lingual Text Classification". In Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP-13).  [pdf]
  
  
• Yuhong Guo and D. Schuurmans (2013),  ``Multi-label Classification with Output Kernels". In Proceedings of the European Conference on Machine Learning (ECML-13).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2013),   ``Online Active Learning for Cost-Sensitive Domain Adaptation". In Proceedings of the Conference on Computational Natural Language Learning (CoNLL-13).  [pdf]
  
  
• X. Li and Yuhong Guo (2013),   ``Active Learning with Multi-label SVM Classification". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-13).  [pdf]  *[code]*
  
  
• Yuhong Guo and W. Xue (2013),   ``Probabilistic Multi-label Classification with Sparse Feature Learning". In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-13).  [pdf]
  
  
• Yuhong Guo (2013),  ``Convex Subspace Representation Learning from Multi-view Data". In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13).  [pdf]
  
  
• X. Li and Yuhong Guo (2013),   ``Adaptive Active Learning for Image Classification". In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-13).  [pdf]  *[code]*
  
  
• M. Xiao and Yuhong Guo (2013),   ``Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model". In Proceedings of the International Conference on Machine Learning (ICML-13).  [pdf]
  
  
• F. Huang, A. Ahuja, D. Downey, Y. Yang, Yuhong Guo and A. Yates (2013),  `` Learning Representations for Weakly Supervised Natural Language Processing Tasks". In Computational linguistics, 2013.  [pdf]
  
  
• L. Lan, N. Djuric, Yuhong Guo and S. Vucetic (2013),  `` MS-kNN: Protein Function Prediction by Integrating Multiple Data Sources". BMC Bioinformatics, Vol. 14(suppl. 3):S8, 2013.  [pdf]
  
  
• P. Radivojac, W.T. Clark, ..., L. Lan, N. Djuric, Yuhong Guo, S. Vucetic, et al. (2013),  `` A Large-scale Evaluation of Computational Protein Function Prediction". Nature Method, Vol. 10(3), pp. 221-229, 2013.  [pdf]
  
  
• Yuhong Guo and M. Xiao (2012),  ``Cross Language Text Classification via Multi-view Subspace Learning". In NIPS workshop on xLiTe: Cross-Lingual Technologies.  
  
  
• M. Xiao and Yuhong Guo (2012),   ``Multi-View AdaBoost for Multilingual Subjectivity Analysis". In Proceedings of the International Conference on Computational Linguistics (COLING-12).  [pdf]
  
  
• M. Xiao, Yuhong Guo and A. Yates (2012),   ``Semi-supervised Representation Learning for Domain Adaptation using Dynamic Dependency Networks". In Proceedings of the International Conference on Computational Linguistics (COLING-12).  [pdf]
  
  
• Q. Liu, Yuhong Guo, J. Wu, and G. Wang (2012),  ``Dynamic Grouping Strategy in Cloud Computing". In Proceedings of the 2nd International Conference on Cloud and Green Computing (CGC-12). (Best Student Paper Award)  [pdf]
  
  
• S. Gu and Yuhong Guo (2012),  ``Max-Margin Ratio Machine". In proceedings of JMLR workshop and conference, Asian Conference on Machine Learning (ACML-12).  [pdf]
  
  
• X. Li and Yuhong Guo (2012),  ``An Object Co-occurrence Assisted Hierarchical Model for Scene Understanding". In Proceedings of the British Machine Vision Conference (BMVC-12).  [pdf]
  
  
• Yuhong Guo and D. Schuurmans (2012),  ``Semi-Supervised Multi-label Classification: A Simultaneous Large-margin, Subspace Learning Approach". In Proceedings of the European Conference on Machine Learning (ECML-12).  [pdf]
  
  
• M. Xiao and Yuhong Guo (2012),  ``Semi-Supervised Kernel Matching for Domain Adaptation". In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12).  [pdf]
  
  
• S. Gu and Yuhong Guo (2012),  ``Learning SVM Classifiers with Indefinite Kernels". In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12). (Outstanding Paper Award)  [pdf]
  
  
• Yuhong Guo and M. Xiao (2012),  ``Transductive Representation Learning for Cross-Lingual Text Classification". In Proceedings of the IEEE International Conference on Data Mining (ICDM-12).  [pdf]
  
  
• Yuhong Guo and M. Xiao (2012),  ``Cross Language Text Classification via Subspace Co-regularized Multi-view Learning". In Proceedings of the Twenty-Nineth International Conference on Machine Learning (ICML-12).  [pdf]
  
  
• V. Ouzienko, Yuhong Guo, and Z. Obradovic (2011),  ``A Decoupled Exponential Random Graph Model for Prediction of Structure and Attributes in Temporal Social Networks". Statistical Analysis and Data Mining Journal.  
  
  
• L. Lan, N. Djuric, Yuhong Guo and S. Vucetic (2011),  ``Protein Function Prediction by Integrating Different Data Sources". AFP/CAFA 2011.  [pdf]
  
  
• Y. Wang, Yuhong Guo and J. Wu (2011),  ``Making Many People Happy: Greedy Solutions for Content Distribution". In Proceedings of the International Conference on Parallel Processing (ICPP-11).  [pdf]
  
  
• Yuhong Guo and D. Schuurmans (2011),  ``Adaptive Large Margin Training for Multilabel Classification". In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-11).  [pdf]
  
  
• Yuhong Guo and S. Gu (2011),  ``Multi-label Classification using Conditional Dependency Networks''. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-11).  [pdf]
  
  
• Yuhong Guo (2010),  ``Active Instance Sampling via Matrix Partition''. In Proceedings of Advances in Neural Information Processing Systems (NIPS-10).  [pdf]
  
  
• Y. Shi, Yuhong Guo, G. Lin, and D. Schuurmans (2010),  ``Kernel-based Gene Regulatory Network Inference''. In Proceedings of the LSS Computational Systems Bioinformatics Conference (CSB-10).  [pdf]
  
  
• K. Rsitovski, D. Das, V. Ouzienko, Yuhong Guo, and Z. Obradovic (2010),  ``Regression Learning with Multiple Noisy Oracles''. In Proceedings of European Conference on Artificail Intelligence (ECAI-10).  [pdf]
  
  
• V. Ouzienko, Yuhong Guo, and Z. Obradovic (2010),  ``Prediction of Attributes and Links in Temporal Social Networks''. In Proceedings of European Conference on Artificail Intelligence (ECAI-10).  [pdf]
  
  
• Yuhong Guo (2009),  ``Supervised Exponential Family PCA via Global Optimization''. [pdf]
  
  
• Yuhong Guo (2009),  ``Max-Margin Multiple-Instance Learning via Semidefinite Programming''. In Advances in Machine Learning, Asian Conference on Machine Learning (ACML-09).  [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2009),  ``A Reformulation of Support Vector Machines for General Confidence Functions". In Advances in Machine Learning, Asian Conference on Machine Learning (ACML-09).  [pdf] 
  
  
• Yuhong Guo (2008),  ``Supervised Exponential Family Principal Component Analysis via Convex Optimization''. In Proceedings of Advances in Neural Information Processing Systems (NIPS-08). [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2008),  ``Efficient Global Optimization for Exponential Family PCA and Low-rank Matrix Factorization". In Allerton Conference on Communication, Control, and Computing (Allerton-08). [pdf] 
  
  
• Yuhong Guo and Dale Schuurmans (2007),  ``Convex Relaxations of Latent Variable Training''. In Proceedings of Advances in Neural Information Processing Systems (NIPS-07).  [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2007),  ``Discriminative Batch Mode Active Learning''. In Proceedings of Advances in Neural Information Processing Systems (NIPS-07).  [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2007),  ``Learning Gene Regulatory Networks via Globally Regularized Risk Minimization''.  In Proceedings of the Fifth Annual RECOMB Satellite Workshop on Comparative Genomics (RECOMB-CG'07).  [pdf]
  
  
• Yuhong Guo and Russ Greiner (2007),  ``Optimistic Active Learning using Mutual Information''.  In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07).  [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2006),  ``Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering''.  In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI-06).  [pdf]
  
  
• Dale Schuurmans, Finnegan Southey, Dana Wilkinson and Yuhong Guo (2006),   ``Metric-based Approaches for Semi-supervised Regression and Classification''.   In O. Chapelle, B. Schoelkopf, and A. Zien, editors, Semi-Supervised Learning, MIT Press. [pdf]
  
  
• Yuhong Guo, Russ Greiner and Dale Schuurmans (2005),   ``Learning Coordination Classifiers".   In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05).  (Distinguished Paper Award) [pdf]
  
  
• Yuhong Guo, Dana Wilkinson and Dale Schuurmans (2005),  ``Maximum Margin Bayesian Networks''.  In Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI-05).  [pdf]
  
  
• Yuhong Guo and Russ Greiner (2005),  ``Discriminative Model Selection for Belief Net Structures".  In Proceedings of the Twentieth AAAI Conference on Artificial Intelligence (AAAI-05).  [pdf]